{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "supported-manner",
   "metadata": {},
   "source": [
    "# A Teaser Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "original-brave",
   "metadata": {},
   "source": [
    "Let's start with a very reduced example that highlights some of the key capabilities of physics-based learning approaches. Let's assume our physical model is a very simple equation: a parabola along the positive x-axis. We'll also directly use this example to give an outlook towards probabilistic \"generative AI\" approaches.\n",
    "\n",
    "Despite being very simple, there are two solutions for every point along x, i.e. we have two _modes_, one above the other one below the x-axis, as shown on the left below. If we don't take care, a conventional learning approach will give us an approximation like the red one shown in the middle, which is completely off. With an improved learning setup, e.g., by using a discretized numerical solver, we can at least accurately represent one of the modes of the solution (shown in green on the right). Interestingly, approaches that learn the full distribution at each point, flow matching as a representative of diffusion models is used below, can capture both modes!\n",
    "\n",
    "```{figure} resources/intro-teaser-side-by-side.jpg\n",
    "---\n",
    "height: 120px\n",
    "name: intro-teaser-side-by-side\n",
    "---\n",
    "Side by side - supervised versus differentiable physics and probabilistic training.\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deadly-paint",
   "metadata": {},
   "source": [
    "## Differentiable physics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "funky-tamil",
   "metadata": {},
   "source": [
    "One of the key concepts of the following chapters is what we'll call _differentiable physics_ (DP). This means that we use domain knowledge in the form of model equations, and then integrate discretized versions of these models into the training process. As implied by the name, having differentiable formulations and operators is crucial for this process to integrate with neural networks training.\n",
    "\n",
    "Let's illustrate the properties of deep learning via DP with the following example: We'd like to find an unknown function $f^*$ that generates solutions from a space $Y$, taking inputs from $X$, i.e. $f^*: X \\to Y$. In the following, we'll often denote _idealized_, and unknown functions with a $*$ superscript, in contrast to their discretized, realizable counterparts without this superscript. Let's additionally assume we have a generic differential equation $\\mathcal P^*: Y \\to Z$ (our _model_ equation), that encodes a property of the solutions, e.g. some real world behavior we'd like to match. Later on, $P^*$ will often represent time evolutions, but it could also be a conservation law (e.g., conservation of mass, then $\\mathcal P^*$ would measure divergence). \n",
    "\n",
    "Using a neural network $f$ to learn the unknown and ideal function $f^*$, we could turn to classic _supervised_ training to obtain $f$ by collecting data. This classical setup requires a dataset by sampling $x$ from $X$ and adding the corresponding solutions $y$ from $Y$. We could obtain these, e.g., by classical numerical techniques. Then we train the NN $f$ with classic methods using this dataset. \n",
    "\n",
    "In contrast to this supervised approach, employing a differentiable physics approach takes advantage of the fact that we can often use a discretized version of the physical model $\\mathcal P$ and employ it to guide the training of $f$. I.e., we want $f$ to be aware of our _simulator_ $\\mathcal P$, and to _interact_ with it. This can give fundamental improvements, as we'll illustrate below with a very simple example (more complex ones will follow later on).\n",
    "\n",
    "Note that in order for the DP approach to work, $\\mathcal P$ has to be _differentiable_, as implied by the name. These differentials, in the form of a gradient, are what's driving the learning process and neural network integration.\n",
    "\n",
    "![Divider](resources/divider-gen1.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recreational-table",
   "metadata": {},
   "source": [
    "## Finding the inverse function of a parabola"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latest-amino",
   "metadata": {},
   "source": [
    "To illustrate the difference of supervised and DP approaches, we consider the following simplified setting: Given the function $\\mathcal P: y\\to y^2$ for $y$ in the interval $[0,1]$, find the unknown function $f$ such that $\\mathcal P(f(x)) = x$ for all $x$ in $[0,1]$. E.g., for $x=0.5$, solutions would be $\\pm\\sqrt{0.5}$.\n",
    "Note: to make things a bit more interesting, we're using $y^2$ here for $\\mathcal P$ instead of the more common $x^2$ parabola, and the _discretization_ is simply given by representing the $x$ and $y$ via floating point numbers in the computer for this simple case.\n",
    "\n",
    "We know that possible solutions for $f$ are the positive or negative square root function (for completeness: piecewise combinations would also be possible).\n",
    "This sounds easy, so let's try to train a neural network to approximate this inverse mapping $f$.\n",
    "Doing this in the \"classical\" supervised manner, i.e. purely based on data, is an obvious starting point. After all, this approach was shown to be a powerful tool for a variety of other applications, e.g., in computer vision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "accompanied-anaheim",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "numerous-emphasis",
   "metadata": {},
   "source": [
    "For supervised training, we can employ our solver $\\mathcal P$ for the problem to pre-compute the solutions we need for training: We randomly choose between the positive and the negative square root. This resembles the  general case, where we would gather all data beforehand, e.g., using optimization techniques to compute the solutions or even experiments. This data collection typically does not favor one particular mode from multimodal solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "realistic-event",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate data\n",
    "N = 10000\n",
    "X = np.random.random(N).astype(np.float32).reshape(-1, 1)\n",
    "\n",
    "# Generation of Y-Data\n",
    "sign = (- np.ones((N,))).astype(np.float32) ** np.random.randint(2, size=N)\n",
    "Y = (np.sqrt(X.flatten()) * sign).reshape(-1, 1).astype(np.float32)\n",
    "\n",
    "# Convert to PyTorch tensors\n",
    "X_tensor = torch.tensor(X)\n",
    "Y_tensor = torch.tensor(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stone-science",
   "metadata": {},
   "source": [
    "Now we can define a network. We'll use a simple fully connected architecture with three hidden layers and ReLU activations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "weighted-costa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the neural network\n",
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self, hiddendim=10):\n",
    "        super(SimpleNN, self).__init__()\n",
    "        self.fc1 = nn.Linear(1, hiddendim)\n",
    "        self.fc2 = nn.Linear(hiddendim, hiddendim)\n",
    "        self.fc3 = nn.Linear(hiddendim, 1)\n",
    "        self.relu = nn.ReLU()\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.relu(self.fc2(x))\n",
    "        x = self.fc3(x)  # Linear output\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confirmed-cargo",
   "metadata": {},
   "source": [
    "Next we can instantiate the model (using a hidden dimension of 128), specify a loss function (will used a simple mean squared error with PyTorch's `MSELoss()`), and the Adam optimizer. The network is trained for 50 epochs in the loop below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "adolescent-yellow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/50, Loss: 0.100193\n",
      "Epoch 20/50, Loss: 0.100193\n",
      "Epoch 30/50, Loss: 0.100207\n",
      "Epoch 40/50, Loss: 0.100202\n",
      "Epoch 50/50, Loss: 0.100203\n"
     ]
    }
   ],
   "source": [
    "nn_sup = SimpleNN(hiddendim=128)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(nn_sup.parameters(), lr=0.001)\n",
    "\n",
    "# Training loop\n",
    "epochs = 50\n",
    "batch_size = 5\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    permutation = torch.randperm(N)\n",
    "    epoch_loss = 0.0\n",
    "    \n",
    "    for i in range(0, N, batch_size):\n",
    "        indices = permutation[i:i+batch_size]\n",
    "        batch_x, batch_y = X_tensor[indices], Y_tensor[indices]\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = nn_sup(batch_x)\n",
    "        loss = criterion(outputs, batch_y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "    \n",
    "    if(epoch%10==9): print(f\"Epoch {epoch+1}/{epochs}, Loss: {epoch_loss/N:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governmental-mixture",
   "metadata": {},
   "source": [
    "As both NN and the data set are fairly small, the training converges quickly. Let's plot the solution: the following one shows the data in light gray, and the supervised solution in red. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sought-basement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(X,Y,'.',label='Datapoints', color=\"lightgray\")\n",
    "plt.plot(X, nn_sup(torch.tensor(X)).detach(), '.',label='T', color=\"red\") \n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Standard approach')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reduced-airplane",
   "metadata": {},
   "source": [
    "😱 This is obviously completely wrong! The red solution is nowhere near one of the two modes of our solution shown in gray.\n",
    "The training process has averaged between the data points on both sides of the x-axis and therefore fails to find satisfying solutions to the problem above.\n",
    "\n",
    "Note that the red line is often not perfectly at zero, which is where the two modes of the solution should average out in the continuous setting. This is caused by the relatively coarse sampling with only 200 points in this example.\n",
    "<br>\n",
    "\n",
    "![Divider](resources/divider-gen2.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dated-requirement",
   "metadata": {},
   "source": [
    "## A differentiable physics approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acoustic-review",
   "metadata": {},
   "source": [
    "Now let's apply the differentiable physics idea as mentioned above to find $f$: we'll directly include our discretized model $\\mathcal P$ in the training. \n",
    "Note that in this context, $\\mathcal P^*$ and $\\mathcal P$ actually provide a mapping back to the input space $X$, i.e. $\\mathcal P^*: Y \\to X$.\n",
    "\n",
    "There is no real data generation step; we only need to sample from the $[0,1]$ interval. We'll simply keep the same $x$ locations used in the previous case, and a new instance of a NN with the same architecture as before `nn_dp`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "extensive-forward",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X-Data\n",
    "# X = X , we can directly re-use the X from above, nothing has changed...\n",
    "\n",
    "# P maps Y back to X, simply by computing a square, as y is a TF tensor input, the square operation **2 will be differentiable\n",
    "def P(y):\n",
    "    return torch.square(y)\n",
    "\n",
    "# Define custom loss function using the \"physics\" operator P\n",
    "def loss_function(x_true, y_pred):\n",
    "    return criterion(x_true, P(y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "conscious-budapest",
   "metadata": {},
   "source": [
    "The loss function is the crucial point for training: we directly incorporate the \"physics\" function to learn, the $\\mathcal P$ computing the squared value, into the loss. The predicted y value squared, as produced by our neural network, gives the X value, and should match the ground-truth `x_true`. Here the `criterion` is simply the mean-squared error between the two, i.e.  $|\\mathcal P(y_{\\text{pred}}) - x_{\\text{true}}|^2$, which we are minimizing during training. \n",
    "\n",
    "Later on, a lot more could happen in $\\mathcal P$ instead of simply a squaring: we could evaluate finite-difference stencils on the predicted solution, or compute a whole implicit time-integration step of a solver. It's not necessary to make it so simple: the more knowledge and numerical methods we can incorporate, the better we can guide the training process. Not that the square function by PyTorch is already differentiable, and hence represents our \"differentiable solver\" in this example.\n",
    "\n",
    "Let's instantiate the neural network again, and train the network with the _differentiable physics_ loss:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "western-leader",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/50, Loss: 0.000004\n",
      "Epoch 20/50, Loss: 0.000003\n",
      "Epoch 30/50, Loss: 0.000003\n",
      "Epoch 40/50, Loss: 0.000002\n",
      "Epoch 50/50, Loss: 0.000002\n"
     ]
    }
   ],
   "source": [
    "nn_dp = SimpleNN(hiddendim=128)\n",
    "optimizer = optim.Adam(nn_dp.parameters(), lr=0.001)\n",
    "\n",
    "# Training loop\n",
    "batch_size = 5\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    permutation = torch.randperm(N)\n",
    "    epoch_loss = 0.0\n",
    "    \n",
    "    for i in range(0, N, batch_size):\n",
    "        indices = permutation[i:i+batch_size]\n",
    "        batch_x = X_tensor[indices]\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = nn_dp(batch_x)\n",
    "        loss = loss_function(batch_x, outputs)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "    \n",
    "    if(epoch%10==9): print(f\"Epoch {epoch+1}/{epochs}, Loss: {epoch_loss/N:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spatial-agency",
   "metadata": {},
   "source": [
    "Now the network actually has learned a good inverse of the parabola function! The following plot shows the solution in green."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "indonesian-abraham",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Results\n",
    "plt.plot(X,Y,'.',label='Datapoints', color=\"lightgray\")\n",
    "plt.plot(X, nn_dp(torch.tensor(X)).detach(), '.',label='T', color=\"green\") \n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Differentiable physics approach')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "star-radio",
   "metadata": {},
   "source": [
    "This looks much better 😎, at least if we're avoiding the origin (this part would need some extra attention).\n",
    "\n",
    "What has happened here?\n",
    "\n",
    "- We've prevented an undesired averaging of multiple modes in the solution by evaluating our discrete model w.r.t. current prediction of the network, rather than using a pre-computed solution. This lets us find the best mode near the network prediction, and prevents an averaging of the modes that exist in the solution manifold.\n",
    "\n",
    "- We're still only getting one side of the curve! This is to be expected because we're representing the solutions with a deterministic function. Hence, we can only represent a single mode. Interestingly, whether it's the top or bottom mode is determined by the random initialization of the weights in $f$ - run the example a couple of times to see this effect in action. To capture multiple modes we'd need to extend the NN to capture the full distribution of the outputs and parametrize it with additional dimensions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "necessary-filename",
   "metadata": {},
   "source": [
    "![Divider](resources/divider-gen3.jpg)\n",
    "\n",
    "\n",
    "# A Probabilistic Generative AI Approach\n",
    "\n",
    "As hinted at above, we can do even better with state of the art AI techniques: we can learn the full _distribution_ of the posterior, in our case the different answers for each $x$. Below, we'll use _flow matching_ as a state of the art approach from generative, diffusion-based algorithms.\n",
    "\n",
    "As these methods work with noisy data, we first need to specify a new dataloader, that adds different amounts of \"noise\" onto the y values of our data, so that the network can learn the right direction towards the two possible modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80a502f5-7b36-4a83-9a02-023d3d229a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "class FlowMatchingDataset(Dataset):\n",
    "    def __init__(self, data_x, data_y, n_samples=1000, sigma_min=1e-4):\n",
    "        super().__init__()\n",
    "        self.n_samples = n_samples\n",
    "        self.sigma_min = sigma_min\n",
    "        self.data_x = data_x\n",
    "        self.data_y = data_y \n",
    "\n",
    "    def __len__(self):\n",
    "        return self.n_samples\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        x0 = np.random.multivariate_normal([0.0, 0.0], np.eye(2), 1)[0]\n",
    "        t = np.random.rand()  # scalar in [0,1]\n",
    "        dx = self.data_x[idx] #:idx+1]\n",
    "        dy_org = self.data_y[idx]# :idx+1]\n",
    "        x0[0] = dx[0] # keep x value\n",
    "        x1 = np.concatenate([dx,dy_org],axis=0)\n",
    "        #print([self.data_x.shape,dx.shape,x1.shape])\n",
    "\n",
    "        x_t = (1 - ( 1 - self.sigma_min) * t) * x0 + t * x1\n",
    "        u_t = (x1 - x0)\n",
    "        x_t = torch.tensor(x_t, dtype=torch.float32)\n",
    "        t   = torch.tensor([t], dtype=torch.float32)\n",
    "        u_t = torch.tensor(u_t, dtype=torch.float32)\n",
    "        return x_t, t, u_t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12554942-2e9f-4d7d-b8e5-bff4ef95a8fe",
   "metadata": {},
   "source": [
    "The network itself is not much different from before, we only need to add an additional time input `t`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e025dc6d-2ca2-4709-b1d4-baf76302dca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class VelocityNet(nn.Module):\n",
    "    def __init__(self, hidden_dim, in_dim=2, time_dim=1, out_dim=2):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(in_dim + time_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, hidden_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_dim, out_dim)\n",
    "        )\n",
    "\n",
    "    def forward(self, x, t):\n",
    "        xt = torch.cat([x, t], dim=1)\n",
    "        return self.net(xt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba9acfa-5a58-4414-8873-0b07129dd217",
   "metadata": {},
   "source": [
    "Training proceeds in line with before, we simply sample noisy samples from the dataset, and train the network to move samples towards the solutions in the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd463858-d0aa-49df-90c3-1be4daa46f30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/50, Loss: 0.3837\n",
      "Epoch 20/50, Loss: 0.3773\n",
      "Epoch 30/50, Loss: 0.3735\n",
      "Epoch 40/50, Loss: 0.3726\n",
      "Epoch 50/50, Loss: 0.3715\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "\n",
    "dataset = FlowMatchingDataset(X, Y, n_samples=N)\n",
    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "nn_fm = VelocityNet(hidden_dim=128).to(device)\n",
    "optimizer = optim.Adam(nn_fm.parameters(), lr=0.001)\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    running_loss = 0.0\n",
    "    for x_t, t, u_t in dataloader:\n",
    "        x_t = x_t.to(device)\n",
    "        t   = t.to(device)\n",
    "        u_t = u_t.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        pred_v = nn_fm(x_t, t)\n",
    "        loss = criterion(pred_v, u_t)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item() * x_t.size(0)\n",
    "    running_loss /= len(dataset)\n",
    "    if(epoch%10==9): print(f\"Epoch {epoch + 1}/{epochs}, Loss: {running_loss:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a7ca01b-b501-4273-930c-2c4bb3d98c41",
   "metadata": {},
   "source": [
    "For evaluation,  we now repeatedly call the neural network to improve an initial noisy  sample drawn from a simple distribution, and step by step move it towards a \"correct\" solution. This is done in the `integrate_flow` function below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1837e971-7dd2-4e74-a9bb-82a21dc04195",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def integrate_flow(nn, x0, t_span=(0.0, 1.0), n_steps=100):\n",
    "    trajectory = []\n",
    "    t = torch.linspace(t_span[0], t_span[1], n_steps).to(x0.device)\n",
    "    dt = 1./n_steps\n",
    "    x_in = x0\n",
    "    for i in range(n_steps):\n",
    "        x0 = x0 + dt * nn(x0, torch.tensor([i/n_steps]).expand(x0.shape[0], 1).to(x0.device) )\n",
    "        x0[:,0] = x_in[:,0] # condition on original x position\n",
    "        trajectory.append(x0)\n",
    "    return trajectory, t\n",
    "\n",
    "# Generate samples along x, then randomize along y\n",
    "n_gen = 500\n",
    "x_in = torch.linspace(0.,1., n_gen).to(device)\n",
    "y_in = torch.randn(n_gen).to(device) * 0.95\n",
    "x0_gen = torch.stack([x_in,y_in],axis=-1)\n",
    "trajectory, time_points = integrate_flow(nn_fm, x0_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ed7951-645b-45ee-b1ab-adada71382d5",
   "metadata": {},
   "source": [
    "To illustrate this flow process, the next cell shows samples at different times in the flow integration. The initial random distribution slowlyl transforms into the bi-modal one for our parabola targets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d8f7d39-7df1-48ef-9ae6-8542c9a692e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_theme(style=\"ticks\", palette=\"pastel\")\n",
    "\n",
    "def get_angle_colors(positions):\n",
    "    angles = np.arctan2(positions[:, 1], positions[:, 0])\n",
    "    angles_deg = (np.degrees(angles) + 360) % 360\n",
    "    colors = np.zeros((len(positions), 3))\n",
    "    for i, angle in enumerate(angles_deg):\n",
    "        segment = int(angle / 120)\n",
    "        local_angle = angle - segment * 120\n",
    "        if segment == 0:    # 0 degrees to 120 degrees (R->G)\n",
    "            colors[i] = [1 - local_angle/120, local_angle/120, 0]\n",
    "        elif segment == 1:  # 120 degrees to 240 degrees (G->B)\n",
    "            colors[i] = [0, 1 - local_angle/120, local_angle/120]\n",
    "        else:               # 240 degrees to 360° (B->R)\n",
    "            colors[i] = [local_angle/120, 0, 1 - local_angle/120]\n",
    "    return colors\n",
    "\n",
    "desired_times = [0.2, 0.6, 0.8,]\n",
    "time_np = time_points.detach().cpu().numpy()\n",
    "n_steps = len(time_np)\n",
    "indices = [np.argmin(np.abs(time_np - t_val)) for t_val in desired_times]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))\n",
    "axes = axes.ravel()  # flatten the 2D array for easier indexing\n",
    "\n",
    "xx, yy = np.mgrid[0:1:100j, -1:1:100j]\n",
    "positions = np.vstack([xx.ravel(), yy.ravel()])\n",
    "\n",
    "for i, idx in enumerate(indices):\n",
    "    ax = axes[i]\n",
    "    x_t = trajectory[idx].detach().cpu().numpy()\n",
    "    if i == 0:\n",
    "        c = get_angle_colors(x_t)\n",
    "    ax.scatter(x_t[:, 0], x_t[:, 1], alpha=0.5, s=10, color=c)\n",
    "    ax.set_title(f\"t = {time_np[idx]:.2f}\")\n",
    "    ax.grid(True)\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447fb175-92ea-4014-9e1a-97271cb3d2fb",
   "metadata": {},
   "source": [
    "Let's also plot the solution in line with the supervised and differentiable physics variants above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd5fd88a-6a3b-4712-bcd6-8d1c250d5769",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Results\n",
    "plt.plot(X,Y,'.',label='Datapoints', color=\"lightgray\")\n",
    "plt.plot(trajectory[-1][:,0].detach().cpu(), trajectory[-1][:,1].detach().cpu(), '.',label='Flow Matching', color=\"orange\") \n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Probabilistic Version')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad4c561-6068-418a-bb53-1a3bf2ebcbdf",
   "metadata": {},
   "source": [
    "\n",
    "As promised, this approach actually resolves both \"modes\" of the solution in the form of points above and below the x-axis. It's still a bit noisy, but this could be alleviated by improving the learning setup, e.g., a larger network would help.\n",
    "\n",
    "An obvious question here also is: we're back to training only with data, how about integrating the physics? That's an obvious point for improvements, and we'll address diffusion-based methods with physical constraints in more detail in a later section. As an outlook: physics-priors can help especially to drive the somewhat noisy output of a neural network towards an accurate solution.\n",
    "\n",
    "![Divider](resources/divider-gen-full.jpg)\n",
    "\n",
    "## Discussion\n",
    "\n",
    "It's a very simple example, but it very clearly shows a failure case for supervised learning. While it might seem very artificial at first sight, many practical PDEs exhibit a variety of these modes, and it's often not clear where (and how many) exist in the solution manifold we're interested in. Using supervised learning is very dangerous in such cases. We might unknowingly get an average of these different modes.\n",
    "\n",
    "Good and obvious examples are bifurcations in fluid flow. Smoke rising above a candle will start out straight, and then, due to tiny perturbations in its motion, start oscillating in a random direction. The images below illustrate this case via _numerical perturbations_: the perfectly symmetric setup will start turning left or right, depending on how the approximation errors build up. Averaging the two modes would give an unphysical, straight flow similar to the parabola example above.\n",
    "\n",
    "Similarly, we have different modes in many numerical solutions, and typically it's important to recover them, rather than averaging them out. Hence, we'll show how to leverage training via _differentiable physics_ in the following chapters for more practical and complex cases.\n",
    "\n",
    "```{figure} resources/intro-fluid-bifurcation.jpg\n",
    "---\n",
    "height: 240px\n",
    "name: intro-fluid-bifurcation \n",
    "---\n",
    "A bifurcation in a buoyancy-driven fluid flow: the \"smoke\" shown in green color starts rising in a perfectly straight manner, but tiny numerical inaccuracies grow over time to lead to an instability with vortices alternating to one side (top-right), or in the opposite direction (bottom-right). \n",
    "```\n"
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    "## Next steps\n",
    "\n",
    "For each of the following notebooks, there's a \"next steps\" section like the one below which contains recommendations about where to start modifying the code. After all, the whole point of these notebooks is to have readily executable programs as a basis for own experiments. The data set and NN sizes of the examples are often quite small to reduce the runtime of the notebooks, but they're nonetheless good starting points for potentially complex and large projects.\n",
    "\n",
    "For the simple DP example above:\n",
    "\n",
    "- This notebook is intentionally using a very simple setup. You can change the training setup or network architectures to improve the solutions. E.g., provide more samples around zero to improve the solution near the origin.\n",
    "\n",
    "- Or try extending the setup to a 2D case, i.e. a paraboloid. Given the function $\\mathcal P:(y_1,y_2)\\to y_1^2+y_2^2$, find an inverse function $f$ such that $\\mathcal P(f(x)) = x$ for all $x$ in $[0,1]$.\n",
    "\n",
    "- If you want to experiment without installing anything, you can also [[run this notebook in colab]](https://colab.research.google.com/github/tum-pbs/pbdl-book/blob/main/intro-teaser.ipynb)."
   ]
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